🧠 Log Analyzer & Attack Detection System — Project Progress Report

Hackathon Track: Cybersecurity & AI-Powered Threat Detection
Repository: log-analyzer-attack-detection
Maintainer: @Rohit30Confluence
Project Start Date: October 25, 2025

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🚀 Project Overview

This project implements a real-time log analysis and attack detection engine, designed to identify malicious activities such as SQL Injection, Cross-Site Scripting (XSS), and Brute-force attacks from application and server logs.
Built with FastAPI, Redis, and Docker, it is structured for scalable real-time deployment and analytics visualization.

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📅 Progress Timeline (Hackathon Development Cycle)

🔹 Day 1 – Initialization (Oct 25, 2025)

• Defined project scope and architecture.
• Created initial repository structure on GitHub.
• Set up .gitignore, requirements.txt, and Dockerfile.
• Initialized FastAPI base with /ping and /health endpoints.

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🔹 Day 2 – Core Detection Engine

• Added log parser for Apache/Nginx-style access logs.
• Implemented signature-based detection:
  • SQL Injection pattern matching.
  • XSS payload detection.
  • Brute-force login attempt identification.
• Modularized detection logic in main.py.

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🔹 Day 3 – Real-Time Queueing & Redis Integration

• Integrated Redis queue (logs_stream) for asynchronous log ingestion.
• Added fallback inline analysis when Redis is not configured.
• Extended /ingest API to accept:
  • Raw text
  • File uploads
  • JSON payloads

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🔹 Day 4 – Deployment & Containerization

• Created Dockerized backend under /backend/Dockerfile.
• Configured environment variables for:
  • REDIS_URL
  • PORT
• Deployed backend to Railway.app using GitHub Actions CI/CD.
• Backend container built successfully — application started via Uvicorn.

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🔹 Day 5 – DNS & Access Issue Investigation

• Despite active deployment, Railway domain unreachable (DNS_PROBE_FINISHED_NXDOMAIN).
• Verified:
  • Healthy container logs (Uvicorn running on 0.0.0.0:8000)
  • No build/runtime errors
  • Port configuration (8000) correct
• Opened detailed GitHub Issue #21 documenting environment, logs, and suspected causes.

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🔹 Day 6 – Repository Enhancements

• Opened and merged feature pull requests:
  • #1: Apache log parser module
  • #2: Attack detection rule modules (SQLi, XSS, Brute Force)
  • #3: Unit tests
  • #4: Visualization logic for detected patterns
  • #5: Adaptive anomaly detection engine
  • #6: CLI interface for unified analysis
• Repository now supports both batch and real-time log analysis workflows.

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🔹 Day 7 – Transition to Real-Time Experimentation

• Preparing for live ingestion loop testing.
• Plan to move deployment from Railway to Render for a stable public endpoint.
• Beginning real-time log intelligence and visualization layer integration.

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⚙️ Current Repository Status

Component	Status	Notes
FastAPI Backend	✅ Functional	Core detection logic stable
Docker Deployment	✅ Working	Container builds successfully
Railway Deployment	⚠️ DNS Error	Active deployment, endpoint unreachable
Redis Integration	⚙️ Optional	Ready for activation
Frontend / Dashboard	🧩 In Progress	Visualization PR pending
Real-Time Experiment	🚧 Upcoming	Next implementation phase

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🎯 Next Steps

1. Redeploy backend on Render / Fly.io to achieve public endpoint availability.
2. Implement live ingestion loop to continuously process incoming logs.
3. Integrate anomaly scoring module for adaptive risk detection.
4. Build visualization dashboard for security insights in real-time.
5. Conduct final test run & benchmarking before hackathon submission.

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📊 Repository Links

• GitHub: Rohit30Confluence/log-analyzer-attack-detection
• Active Issue: #21 – Deployment Active but Application Not Reachable
• Demo Deployment (pending): https://log-analyzer-attack-detection-production.up.railway.app

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🏁 Summary

This repository evolved from a basic FastAPI app to a fully modular real-time log analysis framework ready for cloud-scale attack detection.
Ongoing improvements will complete the intelligence loop, providing autonomous pattern recognition and live monitoring capabilities.